{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n"
     ]
    },
    {
     "data": {
      "image/png": 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yc+Gll2TKhPx8uPlm6dbdtGmgIzusaXKjlFJKVVVhocz59OCDkJICV18t0ya0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OQMEwqFmDx5MnFxcdx6660kJydzww03HL09MfFHJCb+gsaNfwEcuRjhWnbtWkBx8ScUF39CUdFU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\nuBEREZGYonAjIiIiMeX/A6nFOzaBjVNQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1edb12c8e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(__doc__)\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model\n",
    "\n",
    "# X is the 10x10 Hilbert matrix\n",
    "X = 1. / (np.arange(1, 11) + np.arange(0, 10)[:, np.newaxis])\n",
    "y = np.ones(10)\n",
    "\n",
    "# #############################################################################\n",
    "# Compute paths\n",
    "\n",
    "n_alphas = 200\n",
    "alphas = np.logspace(-10, -2, n_alphas)\n",
    "\n",
    "coefs = []\n",
    "for a in alphas:\n",
    "    ridge = linear_model.Ridge(alpha=a, fit_intercept=False)\n",
    "    ridge.fit(X, y)\n",
    "    coefs.append(ridge.coef_)\n",
    "\n",
    "# #############################################################################\n",
    "# Display results\n",
    "\n",
    "ax = plt.gca()\n",
    "\n",
    "ax.plot(alphas, coefs)\n",
    "ax.set_xscale('log')\n",
    "ax.set_xlim(ax.get_xlim()[::-1])  # reverse axis\n",
    "plt.xlabel('alpha')\n",
    "plt.ylabel('weights')\n",
    "plt.title('Ridge coefficients as a function of the regularization')\n",
    "plt.axis('tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n",
      "(3,)\n",
      "(3,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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cqNfrmJmZwapVqxKv7nV3d2Pv3rtw8OBBHDp0KNWxhFSBLO8VIVmhTCaEkM7j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Tft1jAfpX7FBHkllmu66sANPSW3Ors+yxax3V7xDHFkXUZZtohf/OJpY4Jv3usX59OrCItdp/8Z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\nmn7jqIdycLwJkhyo5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U25rfW0hu/XUR8e5dqXzc0tzDI1sKdOw6Zt2zKCP3QQcgHpKbZb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fwSxbJAbZPet8Tn2J8fLWIaRIQLYf8s8J7d9+KSEKMcMnxBAgm86bm3NjIK5t+fZO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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1edb641ab00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(__doc__)\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.feature_selection import f_regression, mutual_info_regression\n",
    "\n",
    "np.random.seed(0)\n",
    "X = np.random.rand(1000, 3)\n",
    "y = X[:, 0] + np.sin(6 * np.pi * X[:, 1]) + 0.1 * np.random.randn(1000)\n",
    "\n",
    "f_test, _ = f_regression(X, y)\n",
    "f_test /= np.max(f_test)\n",
    "\n",
    "mi = mutual_info_regression(X, y)\n",
    "mi /= np.max(mi)\n",
    "\n",
    "plt.figure(figsize=(15, 5))\n",
    "for i in range(3):\n",
    "    plt.subplot(1, 3, i + 1)\n",
    "    plt.scatter(X[:, i], y, edgecolor='black', s=20)\n",
    "    plt.xlabel(\"$x_{}$\".format(i + 1), fontsize=14)\n",
    "    if i == 0:\n",
    "        plt.ylabel(\"$y$\", fontsize=14)\n",
    "    plt.title(\"F-test={:.2f}, MI={:.2f}\".format(f_test[i], mi[i]),\n",
    "              fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n",
      "RandomizedSearchCV took 6.53 seconds for 20 candidates parameter settings.\n",
      "Model with rank: 1\n",
      "Mean validation score: 0.925 (std: 0.019)\n",
      "Parameters: {'max_depth': None, 'bootstrap': False, 'criterion': 'gini', 'max_features': 4, 'min_samples_leaf': 1, 'min_samples_split': 3}\n",
      "\n",
      "Model with rank: 2\n",
      "Mean validation score: 0.922 (std: 0.014)\n",
      "Parameters: {'max_depth': None, 'bootstrap': False, 'criterion': 'entropy', 'max_features': 7, 'min_samples_leaf': 6, 'min_samples_split': 2}\n",
      "\n",
      "Model with rank: 3\n",
      "Mean validation score: 0.901 (std: 0.006)\n",
      "Parameters: {'max_depth': None, 'bootstrap': False, 'criterion': 'gini', 'max_features': 2, 'min_samples_leaf': 10, 'min_samples_split': 10}\n",
      "\n",
      "GridSearchCV took 62.99 seconds for 216 candidate parameter settings.\n",
      "Model with rank: 1\n",
      "Mean validation score: 0.938 (std: 0.015)\n",
      "Parameters: {'max_depth': None, 'bootstrap': False, 'criterion': 'entropy', 'max_features': 10, 'min_samples_leaf': 1, 'min_samples_split': 2}\n",
      "\n",
      "Model with rank: 2\n",
      "Mean validation score: 0.932 (std: 0.006)\n",
      "Parameters: {'max_depth': None, 'bootstrap': False, 'criterion': 'gini', 'max_features': 3, 'min_samples_leaf': 1, 'min_samples_split': 3}\n",
      "\n",
      "Model with rank: 2\n",
      "Mean validation score: 0.932 (std: 0.018)\n",
      "Parameters: {'max_depth': None, 'bootstrap': False, 'criterion': 'entropy', 'max_features': 10, 'min_samples_leaf': 1, 'min_samples_split': 10}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(__doc__)\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from time import time\n",
    "from scipy.stats import randint as sp_randint\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.datasets import load_digits\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# get some data\n",
    "digits = load_digits()\n",
    "X, y = digits.data, digits.target\n",
    "\n",
    "# build a classifier\n",
    "clf = RandomForestClassifier(n_estimators=20)\n",
    "\n",
    "\n",
    "# Utility function to report best scores\n",
    "def report(results, n_top=3):\n",
    "    for i in range(1, n_top + 1):\n",
    "        candidates = np.flatnonzero(results['rank_test_score'] == i)\n",
    "        for candidate in candidates:\n",
    "            print(\"Model with rank: {0}\".format(i))\n",
    "            print(\"Mean validation score: {0:.3f} (std: {1:.3f})\".format(\n",
    "                  results['mean_test_score'][candidate],\n",
    "                  results['std_test_score'][candidate]))\n",
    "            print(\"Parameters: {0}\".format(results['params'][candidate]))\n",
    "            print(\"\")\n",
    "\n",
    "\n",
    "# specify parameters and distributions to sample from\n",
    "param_dist = {\"max_depth\": [3, None],\n",
    "              \"max_features\": sp_randint(1, 11),\n",
    "              \"min_samples_split\": sp_randint(2, 11),\n",
    "              \"min_samples_leaf\": sp_randint(1, 11),\n",
    "              \"bootstrap\": [True, False],\n",
    "              \"criterion\": [\"gini\", \"entropy\"]}\n",
    "\n",
    "# run randomized search\n",
    "n_iter_search = 20\n",
    "random_search = RandomizedSearchCV(clf, param_distributions=param_dist,\n",
    "                                   n_iter=n_iter_search)\n",
    "\n",
    "start = time()\n",
    "random_search.fit(X, y)\n",
    "print(\"RandomizedSearchCV took %.2f seconds for %d candidates\"\n",
    "      \" parameter settings.\" % ((time() - start), n_iter_search))\n",
    "report(random_search.cv_results_)\n",
    "\n",
    "# use a full grid over all parameters\n",
    "param_grid = {\"max_depth\": [3, None],\n",
    "              \"max_features\": [1, 3, 10],\n",
    "              \"min_samples_split\": [2, 3, 10],\n",
    "              \"min_samples_leaf\": [1, 3, 10],\n",
    "              \"bootstrap\": [True, False],\n",
    "              \"criterion\": [\"gini\", \"entropy\"]}\n",
    "\n",
    "# run grid search\n",
    "grid_search = GridSearchCV(clf, param_grid=param_grid)\n",
    "start = time()\n",
    "grid_search.fit(X, y)\n",
    "\n",
    "print(\"GridSearchCV took %.2f seconds for %d candidate parameter settings.\"\n",
    "      % (time() - start, len(grid_search.cv_results_['params'])))\n",
    "report(grid_search.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge,LogisticRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn import preprocessing\n",
    "import pandas as pd\n",
    "\n",
    "def report(results, n_top=3):\n",
    "    for i in range(1, n_top + 1):\n",
    "        candidates = np.flatnonzero(results['rank_test_score'] == i)\n",
    "        for candidate in candidates:\n",
    "            print(\"Model with rank: {0}\".format(i))\n",
    "            print(\"Mean validation score: {0:.8f} (std: {1:.8f})\".format(\n",
    "                  results['mean_test_score'][candidate],\n",
    "                  results['std_test_score'][candidate]))\n",
    "            print(\"Parameters: {0}\".format(results['params'][candidate]))\n",
    "            print(\"\")\n",
    "\n",
    "def grid_search(X, y):\n",
    "    clf = Ridge(tol = 0.01, max_iter = 500, fit_intercept = True)\n",
    "    alpha = np.logspace(-3,2,10)\n",
    "    print(alpha)\n",
    "    param_dist = {\"alpha\":[0.001, 0.01, 0.1],\n",
    "                  \"normalize\": [True, False]}\n",
    "\n",
    "    grid_search = GridSearchCV(Ridge(), param_grid =param_dist, cv=5)\n",
    "    grid_search.fit(X, y)\n",
    "    report(grid_search.cv_results_, n_top=5)\n",
    "    return grid_search.cv_results_\n",
    "\n",
    "def rmsel(true_label,pred):\n",
    "    rmse = np.sqrt(mean_squared_error(true_label, pred))\n",
    "    return 1 / (1 + rmse)\n",
    "    \n",
    "def load_dataset():\n",
    "    data = pd.read_csv('../input/data_stacking.csv')\n",
    "    train = data[data['Score'] != -1]\n",
    "    test  = data[data['Score'] == -1]\n",
    "    test = test.drop(['Score'], axis = 1)\n",
    "    return train, test\n",
    "\n",
    "train, test = load_dataset()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5777169595343619\n"
     ]
    }
   ],
   "source": [
    "X = train.drop(['Id', 'Score'], axis = 1)\n",
    "y = train['Score']\n",
    "\n",
    "clf = LogisticRegression()\n",
    "clf.fit(X, y)\n",
    "\n",
    "y_pred = clf.predict(X)\n",
    "print(rmsel(y_pred, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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